Adaptive Position Update for Geographic Routing in

Mobile Ad Hoc Networks

ABSTRACT:

In geographic routing, nodes need to maintain up-to-date positions of their

immediate neighbors for making effective forwarding decisions. Periodic

broadcasting of beacon packets that contain the geographic location

coordinates of the nodes is a popular method used by most geographic

routing protocols to maintain neighbor positions. We contend and

demonstrate that periodic beaconing regardless of the node mobility and

traffic patterns in the network is not attractive from both update cost and

routing performance points of view. We propose the Adaptive Position

Update (APU) strategy for geographic routing, which dynamically adjusts

the frequency of position updates based on the mobility dynamics of the

nodes and the forwarding patterns in the network. APU is based on two

simple principles: 1) nodes whose movements are harder to predict update

their positions more frequently (and vice versa), and (ii) nodes closer to

forwarding paths update their positions more frequently (and vice versa).

Our theoretical analysis, which is validated by NS2 simulations of a well-

known geographic routing protocol, Greedy Perimeter Stateless Routing

Protocol (GPSR), shows that APU can significantly reduce the update cost

and improve the routing performance in terms of packet delivery ratio and

average end-to-end delay in comparison with periodic beaconing and other

recently proposed updating schemes. The benefits of APU are further

confirmed by undertaking evaluations in realistic network scenarios, which

account for localization error, realistic radio propagation, and sparse

network.

EXISTING SYSTEM:

In geographic routing, the forwarding decision at each node is based on the

locations of the node’s one-hop neighbors and location of the packet

destination as well. A forwarding nodes therefore needs to maintain these

two types of locations. Many works, e.g., GLS, Quorum System, have been

proposed to discover and maintain the location of destination. However, the

maintenance of one-hop neighbors’ location has been often neglected. Some

geographic routing schemes, simply assume that a forwarding node knows

the location of its neighbors. While others use periodical beacon

broadcasting to exchange neighbors’ locations.

In the periodic beaconing scheme, each node broadcasts a beacon with a

fixed beacon interval. If a node does not hear any beacon from a neighbor

for a certain time interval, called neighbor time-out interval, the node

considers this neighbor has moved out of the radio range and removes the

outdated neighbor from its neighbor list. The neighbor time-out interval

often is multiple times of the beacon interval.

DISADVANTAGES OF EXISTING SYSTEM:

Position updates are costly in many ways.

Each update consumes node energy, wireless bandwidth, and increases the

risk of packet collision at the medium access control (MAC) layer.

Packet collisions cause packet loss which in turn affects the routing

performance due to decreased accuracy in determining the correct local

topology (a lost beacon broadcast is not retransmitted).

A lost data packet does get retransmitted, but at the expense of increased

end-to-end delay. Clearly, given the cost associated with transmitting

beacons, it makes sense to adapt the frequency of beacon updates to the node

mobility and the traffic conditions within the network, rather than employing

a static periodic update policy.

For example, if certain nodes are frequently changing their mobility

characteristics (speed and/or heading), it makes sense to frequently

broadcast their updated position. However, for nodes that do not exhibit

significant dynamism, periodic broadcasting of beacons is wasteful. Further,

if only a small percentage of the nodes are involved in forwarding packets, it

is unnecessary for nodes which are located far away from the forwarding

path to employ periodic beaconing because these updates are not useful for

forwarding the current traffic.

PROPOSED SYSTEM:In this paper, we propose a novel beaconing strategy for geographic routing

protocols called Adaptive Position Updates strategy (APU).

APU incorporates two rules for triggering the beacon update process. The

first rule, referred as Mobility Prediction (MP), uses a simple mobility

prediction scheme to estimate when the location information broadcast in the

previous beacon becomes inaccurate. The next beacon is broadcast only if

the predicted error in the location estimate is greater than a certain threshold,

thus tuning the update frequency to the dynamism inherent in the node’s

motion.

The second rule, referred as On-Demand Learning (ODL), aims at

improving the accuracy of the topology along the routing paths between the

communicating nodes. ODL uses an on-demand learning strategy, whereby a

node broadcasts beacons when it overhears the transmission of a data packet

from a new neighbor in its vicinity. This ensures that nodes involved in

forwarding data packets maintain a more up-to date view of the local

topology. On the contrary, nodes that are not in the vicinity of the

forwarding path are unaffected by this rule and do not broadcast beacons

very frequently.

ADVANTAGES OF PROPOSED SYSTEM:

Our scheme eliminates the drawbacks of periodic beaconing by adapting to

the system variations.

The simulation results show that APU can adapt to mobility and traffic load

well. For each dynamic case, APU generates less or similar amount of

beacon overhead as other beaconing schemes but achieve better performance

in terms of packet delivery ratio, average end-to-end delay and energy

consumption. In the second set of simulations, we evaluate the performance

of APU under the consideration of several real-world effects such as a

realistic radio propagation model and localization errors.

The extensive simulation results confirm the superiority of our proposed

scheme over other schemes. The main reason for all these improvements in

APU is that beacons generated in APU are more concentrated along the

routing paths, while the beacons in all other schemes are more scattered in

the whole network. As a result, in APU, the nodes located in the hotspots,

which are responsible for forwarding most of the data traffic in the network

have an up-to-date view of their local topology, thus resulting in improved

performance.

Keypoint :

Packet delivery y ratio

Average end to end delay

Energy consumption calculation

Mobility node prediction:

Packet delivery ratio:

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: System : Pentium IV 2.4 GHz.

Hard Disk : 40 GB.

Monitor : 15 inch VGA Colour.

Mouse : Logitech Mouse.

Ram : 512 MB

Keyboard : Standard Keyboard

SOFTWARE REQUIREMENTS: Operating System : Windows XP.

Coding Language : ASP.NET, C#.Net.

Database : SQL Server 2005